Supervised online matrix factorization for histopathological multimodal retrieval

Victor H. Contreras; Juan S. Lara; Oscar J. Perdomo; Fabio A. González

doi:10.1117/12.2513352

21 December 2018 Supervised online matrix factorization for histopathological multimodal retrieval

Victor H. Contreras, Juan S. Lara, Oscar J. Perdomo, Fabio A. González

Proceedings Volume 10975, 14th International Symposium on Medical Information Processing and Analysis; 109750Y (2018) https://doi.org/10.1117/12.2513352
Event: 14th International Symposium on Medical Information Processing and Analysis, 2018, Mazatlán, Mexico

Abstract

Prostate cancer diagnosis is performed by pathologists through the analysis of tissue samples from the prostate gland using a microscope. The development of automatic acquisition and digitalization technologies has allowed the construction of large collections of digitalized histopathology slides, that are usually accompanied by clinical information and other types of metadata. This collection of cases, along with the metadata, has the potential to be an invaluable resource for the analysis of new challenging cases supporting diagnosis, prognosis, and theragnosis decision tasks. This paper presents a multimodal retrieval system based on a supervised multimodal kernel semantic embedding model that supports the search of relevant cases in a multimodal database, combining both images, i.e. histopathology slides, and text, i.e. pathologist’s reports. The system was tested in a multimodal prostate adenocarcinoma dataset, composed of whole slide images of tissue samples, pathologist’s reports and gradation information using the Gleason score. The system shows a high performance for multimodal information retrieval with a Mean Average Precision of 0.6263.

Citation Download Citation

Victor H. Contreras, Juan S. Lara, Oscar J. Perdomo, and Fabio A. González "Supervised online matrix factorization for histopathological multimodal retrieval", Proc. SPIE 10975, 14th International Symposium on Medical Information Processing and Analysis, 109750Y (21 December 2018); https://doi.org/10.1117/12.2513352

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available